Overview

Dataset statistics

Number of variables18
Number of observations193468
Missing cells738056
Missing cells (%)21.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory26.6 MiB
Average record size in memory144.0 B

Variable types

Numeric6
Categorical7
Text5

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
non_boundary is highly imbalanced (99.9%)Imbalance
is_wicket is highly imbalanced (71.8%)Imbalance
dismissal_kind has 183973 (95.1%) missing valuesMissing
player_dismissed has 183973 (95.1%) missing valuesMissing
fielder has 186684 (96.5%) missing valuesMissing
extras_type has 183235 (94.7%) missing valuesMissing
over has 10254 (5.3%) zerosZeros
batsman_runs has 77637 (40.1%) zerosZeros
extra_runs has 183235 (94.7%) zerosZeros
total_runs has 67841 (35.1%) zerosZeros

Reproduction

Analysis started2024-05-26 05:35:25.530381
Analysis finished2024-05-26 05:35:40.829877
Duration15.3 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

id
Real number (ℝ)

Distinct816
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean756768.81
Minimum335982
Maximum1237181
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:40.992280image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum335982
5-th percentile336023
Q1501227
median729297
Q31082628
95-th percentile1216512
Maximum1237181
Range901199
Interquartile range (IQR)581401

Descriptive statistics

Standard deviation306097.09
Coefficient of variation (CV)0.404479
Kurtosis-1.4904049
Mean756768.81
Median Absolute Deviation (MAD)310139
Skewness0.21218723
Sum1.4641055 × 1011
Variance9.3695429 × 1010
MonotonicityIncreasing
2024-05-26T11:05:41.352857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
829737 262
 
0.1%
829811 259
 
0.1%
1216522 259
 
0.1%
1178423 257
 
0.1%
419142 257
 
0.1%
734047 257
 
0.1%
501221 257
 
0.1%
548367 256
 
0.1%
392190 256
 
0.1%
829805 256
 
0.1%
Other values (806) 190892
98.7%
ValueCountFrequency (%)
335982 225
0.1%
335983 248
0.1%
335984 219
0.1%
335985 246
0.1%
335986 240
0.1%
335987 241
0.1%
335988 205
0.1%
335989 255
0.1%
335990 248
0.1%
335991 250
0.1%
ValueCountFrequency (%)
1237181 235
0.1%
1237180 250
0.1%
1237178 247
0.1%
1237177 247
0.1%
1216547 248
0.1%
1216546 243
0.1%
1216545 235
0.1%
1216544 240
0.1%
1216543 252
0.1%
1216542 228
0.1%

inning
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
1
100191 
2
93277 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters193468
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

Length

2024-05-26T11:05:41.566972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T11:05:41.737066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

Most occurring characters

ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 100191
51.8%
2 93277
48.2%

over
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1770267
Minimum0
Maximum19
Zeros10254
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:41.897427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q314
95-th percentile18
Maximum19
Range19
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6768479
Coefficient of variation (CV)0.61859337
Kurtosis-1.1835537
Mean9.1770267
Median Absolute Deviation (MAD)5
Skewness0.046966973
Sum1775461
Variance32.226602
MonotonicityNot monotonic
2024-05-26T11:05:42.091550image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
1 10255
 
5.3%
0 10254
 
5.3%
2 10155
 
5.2%
3 10115
 
5.2%
4 10092
 
5.2%
5 10067
 
5.2%
6 10021
 
5.2%
7 9993
 
5.2%
8 9964
 
5.2%
9 9920
 
5.1%
Other values (10) 92632
47.9%
ValueCountFrequency (%)
0 10254
5.3%
1 10255
5.3%
2 10155
5.2%
3 10115
5.2%
4 10092
5.2%
5 10067
5.2%
6 10021
5.2%
7 9993
5.2%
8 9964
5.2%
9 9920
5.1%
ValueCountFrequency (%)
19 7327
3.8%
18 8531
4.4%
17 9086
4.7%
16 9358
4.8%
15 9477
4.9%
14 9638
5.0%
13 9712
5.0%
12 9806
5.1%
11 9833
5.1%
10 9864
5.1%

ball
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6159675
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:42.290171image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q35
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8071276
Coefficient of variation (CV)0.49976323
Kurtosis-1.0818689
Mean3.6159675
Median Absolute Deviation (MAD)2
Skewness0.096434009
Sum699574
Variance3.2657102
MonotonicityNot monotonic
2024-05-26T11:05:42.476318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 31372
16.2%
2 31285
16.2%
3 31200
16.1%
4 31129
16.1%
5 31032
16.0%
6 30929
16.0%
7 5521
 
2.9%
8 865
 
0.4%
9 135
 
0.1%
ValueCountFrequency (%)
1 31372
16.2%
2 31285
16.2%
3 31200
16.1%
4 31129
16.1%
5 31032
16.0%
6 30929
16.0%
7 5521
 
2.9%
8 865
 
0.4%
9 135
 
0.1%
ValueCountFrequency (%)
9 135
 
0.1%
8 865
 
0.4%
7 5521
 
2.9%
6 30929
16.0%
5 31032
16.0%
4 31129
16.1%
3 31200
16.1%
2 31285
16.2%
1 31372
16.2%
Distinct537
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:42.930307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length9.3481661
Min length5

Characters and Unicode

Total characters1808571
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowRT Ponting
2nd rowBB McCullum
3rd rowBB McCullum
4th rowBB McCullum
5th rowRT Ponting
ValueCountFrequency (%)
v 6971
 
1.8%
s 6761
 
1.7%
da 5193
 
1.3%
singh 5156
 
1.3%
sharma 5021
 
1.3%
de 5017
 
1.3%
sr 4932
 
1.2%
kohli 4629
 
1.2%
smith 4352
 
1.1%
dhawan 4343
 
1.1%
Other values (732) 344499
86.8%
2024-05-26T11:05:43.663397image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
203406
 
11.2%
a 199877
 
11.1%
i 87819
 
4.9%
n 84273
 
4.7%
h 81996
 
4.5%
r 77511
 
4.3%
S 73822
 
4.1%
e 73267
 
4.1%
l 68462
 
3.8%
s 48463
 
2.7%
Other values (44) 809675
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1808571
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
203406
 
11.2%
a 199877
 
11.1%
i 87819
 
4.9%
n 84273
 
4.7%
h 81996
 
4.5%
r 77511
 
4.3%
S 73822
 
4.1%
e 73267
 
4.1%
l 68462
 
3.8%
s 48463
 
2.7%
Other values (44) 809675
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1808571
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
203406
 
11.2%
a 199877
 
11.1%
i 87819
 
4.9%
n 84273
 
4.7%
h 81996
 
4.5%
r 77511
 
4.3%
S 73822
 
4.1%
e 73267
 
4.1%
l 68462
 
3.8%
s 48463
 
2.7%
Other values (44) 809675
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1808571
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
203406
 
11.2%
a 199877
 
11.1%
i 87819
 
4.9%
n 84273
 
4.7%
h 81996
 
4.5%
r 77511
 
4.3%
S 73822
 
4.1%
e 73267
 
4.1%
l 68462
 
3.8%
s 48463
 
2.7%
Other values (44) 809675
44.8%
Distinct530
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:44.101029image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length9.3539759
Min length5

Characters and Unicode

Total characters1809695
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowBB McCullum
2nd rowRT Ponting
3rd rowRT Ponting
4th rowRT Ponting
5th rowBB McCullum
ValueCountFrequency (%)
s 7062
 
1.8%
v 6966
 
1.8%
sharma 5170
 
1.3%
sr 5163
 
1.3%
de 4926
 
1.2%
da 4910
 
1.2%
singh 4908
 
1.2%
dhawan 4712
 
1.2%
mk 4498
 
1.1%
kohli 4481
 
1.1%
Other values (729) 344108
86.7%
2024-05-26T11:05:44.864360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
203436
 
11.2%
a 201530
 
11.1%
i 87712
 
4.8%
n 84281
 
4.7%
h 82046
 
4.5%
r 77246
 
4.3%
S 74021
 
4.1%
e 73939
 
4.1%
l 68025
 
3.8%
s 48279
 
2.7%
Other values (44) 809180
44.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1809695
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
203436
 
11.2%
a 201530
 
11.1%
i 87712
 
4.8%
n 84281
 
4.7%
h 82046
 
4.5%
r 77246
 
4.3%
S 74021
 
4.1%
e 73939
 
4.1%
l 68025
 
3.8%
s 48279
 
2.7%
Other values (44) 809180
44.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1809695
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
203436
 
11.2%
a 201530
 
11.1%
i 87712
 
4.8%
n 84281
 
4.7%
h 82046
 
4.5%
r 77246
 
4.3%
S 74021
 
4.1%
e 73939
 
4.1%
l 68025
 
3.8%
s 48279
 
2.7%
Other values (44) 809180
44.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1809695
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
203436
 
11.2%
a 201530
 
11.1%
i 87712
 
4.8%
n 84281
 
4.7%
h 82046
 
4.5%
r 77246
 
4.3%
S 74021
 
4.1%
e 73939
 
4.1%
l 68025
 
3.8%
s 48279
 
2.7%
Other values (44) 809180
44.7%

bowler
Text

Distinct420
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:45.353906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length17
Mean length9.563442
Min length5

Characters and Unicode

Total characters1850220
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowAA Noffke
2nd rowAA Noffke
3rd rowZ Khan
4th rowZ Khan
5th rowZ Khan
ValueCountFrequency (%)
r 10253
 
2.6%
sharma 9728
 
2.5%
singh 9401
 
2.4%
a 8955
 
2.3%
kumar 7658
 
1.9%
s 6693
 
1.7%
m 6334
 
1.6%
pp 5230
 
1.3%
p 4727
 
1.2%
patel 4401
 
1.1%
Other values (598) 320729
81.4%
2024-05-26T11:05:46.093113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 236556
 
12.8%
200641
 
10.8%
r 98106
 
5.3%
n 98028
 
5.3%
h 95874
 
5.2%
i 81714
 
4.4%
e 79364
 
4.3%
S 71805
 
3.9%
l 58168
 
3.1%
M 48769
 
2.6%
Other values (45) 781195
42.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1850220
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 236556
 
12.8%
200641
 
10.8%
r 98106
 
5.3%
n 98028
 
5.3%
h 95874
 
5.2%
i 81714
 
4.4%
e 79364
 
4.3%
S 71805
 
3.9%
l 58168
 
3.1%
M 48769
 
2.6%
Other values (45) 781195
42.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1850220
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 236556
 
12.8%
200641
 
10.8%
r 98106
 
5.3%
n 98028
 
5.3%
h 95874
 
5.2%
i 81714
 
4.4%
e 79364
 
4.3%
S 71805
 
3.9%
l 58168
 
3.1%
M 48769
 
2.6%
Other values (45) 781195
42.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1850220
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 236556
 
12.8%
200641
 
10.8%
r 98106
 
5.3%
n 98028
 
5.3%
h 95874
 
5.2%
i 81714
 
4.4%
e 79364
 
4.3%
S 71805
 
3.9%
l 58168
 
3.1%
M 48769
 
2.6%
Other values (45) 781195
42.2%

batsman_runs
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2402309
Minimum0
Maximum6
Zeros77637
Zeros (%)40.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:46.288826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.6108666
Coefficient of variation (CV)1.298844
Kurtosis1.6411513
Mean1.2402309
Median Absolute Deviation (MAD)1
Skewness1.5869113
Sum239945
Variance2.594891
MonotonicityNot monotonic
2024-05-26T11:05:46.447990image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 77637
40.1%
1 71937
37.2%
4 21908
 
11.3%
2 12408
 
6.4%
6 8902
 
4.6%
3 616
 
0.3%
5 60
 
< 0.1%
ValueCountFrequency (%)
0 77637
40.1%
1 71937
37.2%
2 12408
 
6.4%
3 616
 
0.3%
4 21908
 
11.3%
5 60
 
< 0.1%
6 8902
 
4.6%
ValueCountFrequency (%)
6 8902
 
4.6%
5 60
 
< 0.1%
4 21908
 
11.3%
3 616
 
0.3%
2 12408
 
6.4%
1 71937
37.2%
0 77637
40.1%

extra_runs
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.066414084
Minimum0
Maximum7
Zeros183235
Zeros (%)94.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:46.624336image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.33999134
Coefficient of variation (CV)5.1192656
Kurtosis91.669822
Mean0.066414084
Median Absolute Deviation (MAD)0
Skewness8.2427353
Sum12849
Variance0.11559411
MonotonicityNot monotonic
2024-05-26T11:05:46.792607image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 183235
94.7%
1 9120
 
4.7%
2 440
 
0.2%
4 368
 
0.2%
5 229
 
0.1%
3 75
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 183235
94.7%
1 9120
 
4.7%
2 440
 
0.2%
3 75
 
< 0.1%
4 368
 
0.2%
5 229
 
0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 229
 
0.1%
4 368
 
0.2%
3 75
 
< 0.1%
2 440
 
0.2%
1 9120
 
4.7%
0 183235
94.7%

total_runs
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.306645
Minimum0
Maximum7
Zeros67841
Zeros (%)35.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2024-05-26T11:05:46.963664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile4
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5988018
Coefficient of variation (CV)1.223593
Kurtosis1.5845302
Mean1.306645
Median Absolute Deviation (MAD)1
Skewness1.5575928
Sum252794
Variance2.5561671
MonotonicityNot monotonic
2024-05-26T11:05:47.134121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 80368
41.5%
0 67841
35.1%
4 22187
 
11.5%
2 13056
 
6.7%
6 8850
 
4.6%
3 735
 
0.4%
5 378
 
0.2%
7 53
 
< 0.1%
ValueCountFrequency (%)
0 67841
35.1%
1 80368
41.5%
2 13056
 
6.7%
3 735
 
0.4%
4 22187
 
11.5%
5 378
 
0.2%
6 8850
 
4.6%
7 53
 
< 0.1%
ValueCountFrequency (%)
7 53
 
< 0.1%
6 8850
 
4.6%
5 378
 
0.2%
4 22187
 
11.5%
3 735
 
0.4%
2 13056
 
6.7%
1 80368
41.5%
0 67841
35.1%

non_boundary
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
193452 
1
 
16

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters193468
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

Length

2024-05-26T11:05:47.317143image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T11:05:47.474533image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 193452
> 99.9%
1 16
 
< 0.1%

is_wicket
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
0
183973 
1
 
9495

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters193468
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

Length

2024-05-26T11:05:47.662155image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T11:05:47.818469image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

Most occurring characters

ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 193468
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 183973
95.1%
1 9495
 
4.9%

dismissal_kind
Categorical

MISSING 

Distinct9
Distinct (%)0.1%
Missing183973
Missing (%)95.1%
Memory size1.5 MiB
caught
5743 
bowled
1700 
run out
893 
lbw
 
571
stumped
 
294
Other values (4)
 
294

Length

Max length21
Median length6
Mean length6.271406
Min length3

Characters and Unicode

Total characters59547
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowcaught
2nd rowcaught
3rd rowcaught
4th rowcaught
5th rowcaught

Common Values

ValueCountFrequency (%)
caught 5743
 
3.0%
bowled 1700
 
0.9%
run out 893
 
0.5%
lbw 571
 
0.3%
stumped 294
 
0.2%
caught and bowled 269
 
0.1%
hit wicket 12
 
< 0.1%
retired hurt 11
 
< 0.1%
obstructing the field 2
 
< 0.1%
(Missing) 183973
95.1%

Length

2024-05-26T11:05:48.003743image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T11:05:48.194468image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
caught 6012
54.9%
bowled 1969
 
18.0%
run 893
 
8.2%
out 893
 
8.2%
lbw 571
 
5.2%
stumped 294
 
2.7%
and 269
 
2.5%
hit 12
 
0.1%
wicket 12
 
0.1%
retired 11
 
0.1%
Other values (4) 17
 
0.2%

Most occurring characters

ValueCountFrequency (%)
u 8105
13.6%
t 7251
12.2%
a 6281
10.5%
h 6037
10.1%
c 6026
10.1%
g 6014
10.1%
o 2864
 
4.8%
w 2552
 
4.3%
d 2545
 
4.3%
b 2542
 
4.3%
Other values (11) 9330
15.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 59547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 8105
13.6%
t 7251
12.2%
a 6281
10.5%
h 6037
10.1%
c 6026
10.1%
g 6014
10.1%
o 2864
 
4.8%
w 2552
 
4.3%
d 2545
 
4.3%
b 2542
 
4.3%
Other values (11) 9330
15.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 59547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 8105
13.6%
t 7251
12.2%
a 6281
10.5%
h 6037
10.1%
c 6026
10.1%
g 6014
10.1%
o 2864
 
4.8%
w 2552
 
4.3%
d 2545
 
4.3%
b 2542
 
4.3%
Other values (11) 9330
15.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 59547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 8105
13.6%
t 7251
12.2%
a 6281
10.5%
h 6037
10.1%
c 6026
10.1%
g 6014
10.1%
o 2864
 
4.8%
w 2552
 
4.3%
d 2545
 
4.3%
b 2542
 
4.3%
Other values (11) 9330
15.7%

player_dismissed
Text

MISSING 

Distinct506
Distinct (%)5.3%
Missing183973
Missing (%)95.1%
Memory size1.5 MiB
2024-05-26T11:05:48.648580image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length23
Median length20
Mean length9.3817799
Min length5

Characters and Unicode

Total characters89080
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)0.9%

Sample

1st rowRT Ponting
2nd rowDJ Hussey
3rd rowSC Ganguly
4th rowMV Boucher
5th rowB Akhil
ValueCountFrequency (%)
singh 327
 
1.7%
s 305
 
1.6%
v 276
 
1.4%
sharma 256
 
1.3%
r 253
 
1.3%
m 228
 
1.2%
de 200
 
1.0%
patel 199
 
1.0%
sr 194
 
1.0%
sk 188
 
1.0%
Other values (693) 17029
87.5%
2024-05-26T11:05:49.341728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 10107
 
11.3%
9960
 
11.2%
i 4252
 
4.8%
h 4167
 
4.7%
n 4151
 
4.7%
r 3899
 
4.4%
e 3592
 
4.0%
S 3570
 
4.0%
l 3189
 
3.6%
A 2300
 
2.6%
Other values (44) 39893
44.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 89080
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 10107
 
11.3%
9960
 
11.2%
i 4252
 
4.8%
h 4167
 
4.7%
n 4151
 
4.7%
r 3899
 
4.4%
e 3592
 
4.0%
S 3570
 
4.0%
l 3189
 
3.6%
A 2300
 
2.6%
Other values (44) 39893
44.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 89080
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 10107
 
11.3%
9960
 
11.2%
i 4252
 
4.8%
h 4167
 
4.7%
n 4151
 
4.7%
r 3899
 
4.4%
e 3592
 
4.0%
S 3570
 
4.0%
l 3189
 
3.6%
A 2300
 
2.6%
Other values (44) 39893
44.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 89080
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 10107
 
11.3%
9960
 
11.2%
i 4252
 
4.8%
h 4167
 
4.7%
n 4151
 
4.7%
r 3899
 
4.4%
e 3592
 
4.0%
S 3570
 
4.0%
l 3189
 
3.6%
A 2300
 
2.6%
Other values (44) 39893
44.8%

fielder
Text

MISSING 

Distinct879
Distinct (%)13.0%
Missing186684
Missing (%)96.5%
Memory size1.5 MiB
2024-05-26T11:05:49.999460image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length41
Median length33
Mean length10.164062
Min length5

Characters and Unicode

Total characters68953
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique417 ?
Unique (%)6.1%

Sample

1st rowP Kumar
2nd rowCL White
3rd rowJH Kallis
4th rowM Kartik
5th rowRT Ponting
ValueCountFrequency (%)
de 215
 
1.5%
ms 207
 
1.4%
singh 207
 
1.4%
r 205
 
1.4%
karthik 197
 
1.4%
m 189
 
1.3%
dhoni 186
 
1.3%
sharma 185
 
1.3%
patel 178
 
1.2%
s 168
 
1.2%
Other values (1008) 12584
86.7%
2024-05-26T11:05:50.787997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 7766
 
11.3%
7737
 
11.2%
i 3398
 
4.9%
h 3304
 
4.8%
r 3016
 
4.4%
n 2982
 
4.3%
e 2705
 
3.9%
S 2600
 
3.8%
l 2399
 
3.5%
K 1812
 
2.6%
Other values (46) 31234
45.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68953
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7766
 
11.3%
7737
 
11.2%
i 3398
 
4.9%
h 3304
 
4.8%
r 3016
 
4.4%
n 2982
 
4.3%
e 2705
 
3.9%
S 2600
 
3.8%
l 2399
 
3.5%
K 1812
 
2.6%
Other values (46) 31234
45.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68953
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7766
 
11.3%
7737
 
11.2%
i 3398
 
4.9%
h 3304
 
4.8%
r 3016
 
4.4%
n 2982
 
4.3%
e 2705
 
3.9%
S 2600
 
3.8%
l 2399
 
3.5%
K 1812
 
2.6%
Other values (46) 31234
45.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68953
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7766
 
11.3%
7737
 
11.2%
i 3398
 
4.9%
h 3304
 
4.8%
r 3016
 
4.4%
n 2982
 
4.3%
e 2705
 
3.9%
S 2600
 
3.8%
l 2399
 
3.5%
K 1812
 
2.6%
Other values (46) 31234
45.3%

extras_type
Categorical

MISSING 

Distinct5
Distinct (%)< 0.1%
Missing183235
Missing (%)94.7%
Memory size1.5 MiB
wides
5858 
legbyes
3107 
noballs
758 
byes
 
508
penalty
 
2

Length

Max length7
Median length5
Mean length5.7061468
Min length4

Characters and Unicode

Total characters58391
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbyes
2nd rowwides
3rd rowwides
4th rowlegbyes
5th rowwides

Common Values

ValueCountFrequency (%)
wides 5858
 
3.0%
legbyes 3107
 
1.6%
noballs 758
 
0.4%
byes 508
 
0.3%
penalty 2
 
< 0.1%
(Missing) 183235
94.7%

Length

2024-05-26T11:05:51.042089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-26T11:05:51.223500image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
wides 5858
57.2%
legbyes 3107
30.4%
noballs 758
 
7.4%
byes 508
 
5.0%
penalty 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 12582
21.5%
s 10231
17.5%
w 5858
10.0%
i 5858
10.0%
d 5858
10.0%
l 4625
 
7.9%
b 4373
 
7.5%
y 3617
 
6.2%
g 3107
 
5.3%
n 760
 
1.3%
Other values (4) 1522
 
2.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 58391
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 12582
21.5%
s 10231
17.5%
w 5858
10.0%
i 5858
10.0%
d 5858
10.0%
l 4625
 
7.9%
b 4373
 
7.5%
y 3617
 
6.2%
g 3107
 
5.3%
n 760
 
1.3%
Other values (4) 1522
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 58391
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 12582
21.5%
s 10231
17.5%
w 5858
10.0%
i 5858
10.0%
d 5858
10.0%
l 4625
 
7.9%
b 4373
 
7.5%
y 3617
 
6.2%
g 3107
 
5.3%
n 760
 
1.3%
Other values (4) 1522
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 58391
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 12582
21.5%
s 10231
17.5%
w 5858
10.0%
i 5858
10.0%
d 5858
10.0%
l 4625
 
7.9%
b 4373
 
7.5%
y 3617
 
6.2%
g 3107
 
5.3%
n 760
 
1.3%
Other values (4) 1522
 
2.6%

batting_team
Categorical

Distinct15
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Mumbai Indians
24466 
Royal Challengers Bangalore
22706 
Kings XI Punjab
22622 
Kolkata Knight Riders
22554 
Chennai Super Kings
21455 
Other values (10)
79665 

Length

Max length27
Median length22
Mean length17.987719
Min length13

Characters and Unicode

Total characters3480048
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKolkata Knight Riders
2nd rowKolkata Knight Riders
3rd rowKolkata Knight Riders
4th rowKolkata Knight Riders
5th rowKolkata Knight Riders

Common Values

ValueCountFrequency (%)
Mumbai Indians 24466
12.6%
Royal Challengers Bangalore 22706
11.7%
Kings XI Punjab 22622
11.7%
Kolkata Knight Riders 22554
11.7%
Chennai Super Kings 21455
11.1%
Rajasthan Royals 18954
9.8%
Delhi Daredevils 18780
9.7%
Sunrisers Hyderabad 14826
7.7%
Deccan Chargers 9034
 
4.7%
Pune Warriors 5443
 
2.8%
Other values (5) 12628
6.5%

Length

2024-05-26T11:05:51.431648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 44077
 
9.2%
mumbai 24466
 
5.1%
indians 24466
 
5.1%
delhi 22788
 
4.7%
royal 22706
 
4.7%
challengers 22706
 
4.7%
bangalore 22706
 
4.7%
xi 22622
 
4.7%
punjab 22622
 
4.7%
kolkata 22554
 
4.7%
Other values (22) 229622
47.7%

Most occurring characters

ValueCountFrequency (%)
a 398476
 
11.5%
n 288762
 
8.3%
287867
 
8.3%
e 257217
 
7.4%
i 240997
 
6.9%
s 230410
 
6.6%
r 197278
 
5.7%
l 179490
 
5.2%
g 128037
 
3.7%
h 119073
 
3.4%
Other values (27) 1152441
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3480048
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 398476
 
11.5%
n 288762
 
8.3%
287867
 
8.3%
e 257217
 
7.4%
i 240997
 
6.9%
s 230410
 
6.6%
r 197278
 
5.7%
l 179490
 
5.2%
g 128037
 
3.7%
h 119073
 
3.4%
Other values (27) 1152441
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3480048
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 398476
 
11.5%
n 288762
 
8.3%
287867
 
8.3%
e 257217
 
7.4%
i 240997
 
6.9%
s 230410
 
6.6%
r 197278
 
5.7%
l 179490
 
5.2%
g 128037
 
3.7%
h 119073
 
3.4%
Other values (27) 1152441
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3480048
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 398476
 
11.5%
n 288762
 
8.3%
287867
 
8.3%
e 257217
 
7.4%
i 240997
 
6.9%
s 230410
 
6.6%
r 197278
 
5.7%
l 179490
 
5.2%
g 128037
 
3.7%
h 119073
 
3.4%
Other values (27) 1152441
33.1%

bowling_team
Categorical

Distinct15
Distinct (%)< 0.1%
Missing191
Missing (%)0.1%
Memory size1.5 MiB
Mumbai Indians
24453 
Royal Challengers Bangalore
23024 
Kolkata Knight Riders
22583 
Kings XI Punjab
22457 
Chennai Super Kings
21224 
Other values (10)
79536 

Length

Max length27
Median length22
Mean length18.007797
Min length13

Characters and Unicode

Total characters3480493
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRoyal Challengers Bangalore
2nd rowRoyal Challengers Bangalore
3rd rowRoyal Challengers Bangalore
4th rowRoyal Challengers Bangalore
5th rowRoyal Challengers Bangalore

Common Values

ValueCountFrequency (%)
Mumbai Indians 24453
12.6%
Royal Challengers Bangalore 23024
11.9%
Kolkata Knight Riders 22583
11.7%
Kings XI Punjab 22457
11.6%
Chennai Super Kings 21224
11.0%
Rajasthan Royals 18972
9.8%
Delhi Daredevils 18719
9.7%
Sunrisers Hyderabad 14703
7.6%
Deccan Chargers 9039
 
4.7%
Pune Warriors 5394
 
2.8%
Other values (5) 12709
6.6%

Length

2024-05-26T11:05:51.659464image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
kings 43681
 
9.1%
mumbai 24453
 
5.1%
indians 24453
 
5.1%
royal 23024
 
4.8%
challengers 23024
 
4.8%
bangalore 23024
 
4.8%
delhi 22731
 
4.7%
kolkata 22583
 
4.7%
knight 22583
 
4.7%
riders 22583
 
4.7%
Other values (22) 228860
47.6%

Most occurring characters

ValueCountFrequency (%)
a 399209
 
11.5%
n 288400
 
8.3%
287722
 
8.3%
e 257464
 
7.4%
i 240319
 
6.9%
s 230181
 
6.6%
r 197254
 
5.7%
l 180727
 
5.2%
g 128437
 
3.7%
h 119187
 
3.4%
Other values (27) 1151593
33.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3480493
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 399209
 
11.5%
n 288400
 
8.3%
287722
 
8.3%
e 257464
 
7.4%
i 240319
 
6.9%
s 230181
 
6.6%
r 197254
 
5.7%
l 180727
 
5.2%
g 128437
 
3.7%
h 119187
 
3.4%
Other values (27) 1151593
33.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3480493
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 399209
 
11.5%
n 288400
 
8.3%
287722
 
8.3%
e 257464
 
7.4%
i 240319
 
6.9%
s 230181
 
6.6%
r 197254
 
5.7%
l 180727
 
5.2%
g 128437
 
3.7%
h 119187
 
3.4%
Other values (27) 1151593
33.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3480493
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 399209
 
11.5%
n 288400
 
8.3%
287722
 
8.3%
e 257464
 
7.4%
i 240319
 
6.9%
s 230181
 
6.6%
r 197254
 
5.7%
l 180727
 
5.2%
g 128437
 
3.7%
h 119187
 
3.4%
Other values (27) 1151593
33.1%

Interactions

2024-05-26T11:05:37.721369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:31.619873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:32.872686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:34.100015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:35.265907image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:36.465014image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:37.922546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:31.888824image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:33.082116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:34.282613image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:35.443472image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:36.653116image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:38.114972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:32.076162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:33.295815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:34.485700image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:35.685433image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:36.961575image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:38.316752image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:32.267705image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:33.521017image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:34.692094image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:35.890282image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:37.155073image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:38.503229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:32.466491image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:33.709508image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:34.880071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:36.083309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:37.330991image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:38.691323image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:32.666897image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:33.900517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:35.070054image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:36.275807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-05-26T11:05:37.527560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-05-26T11:05:39.018312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-26T11:05:39.688121image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-05-26T11:05:40.540666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

idinningoverballbatsmannon_strikerbowlerbatsman_runsextra_runstotal_runsnon_boundaryis_wicketdismissal_kindplayer_dismissedfielderextras_typebatting_teambowling_team
0335982165RT PontingBB McCullumAA Noffke10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
1335982166BB McCullumRT PontingAA Noffke10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
2335982171BB McCullumRT PontingZ Khan00000NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
3335982172BB McCullumRT PontingZ Khan10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
4335982173RT PontingBB McCullumZ Khan10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
5335982174BB McCullumRT PontingZ Khan10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
6335982175RT PontingBB McCullumZ Khan10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
7335982176BB McCullumRT PontingZ Khan10100NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
8335982181BB McCullumRT PontingJH Kallis00000NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
9335982182BB McCullumRT PontingJH Kallis00000NaNNaNNaNNaNKolkata Knight RidersRoyal Challengers Bangalore
idinningoverballbatsmannon_strikerbowlerbatsman_runsextra_runstotal_runsnon_boundaryis_wicketdismissal_kindplayer_dismissedfielderextras_typebatting_teambowling_team
19345812371811117SS IyerRR PantKA Pollard60600NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19345912371811121RR PantSS IyerNM Coulter-Nile10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346012371811122SS IyerRR PantNM Coulter-Nile10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346112371811123RR PantSS IyerNM Coulter-Nile10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346212371811124SS IyerRR PantNM Coulter-Nile10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346312371811125RR PantSS IyerNM Coulter-Nile00000NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346412371811126RR PantSS IyerNM Coulter-Nile10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346512371811131RR PantSS IyerKH Pandya01100NaNNaNNaNwidesDelhi CapitalsMumbai Indians
19346612371811132RR PantSS IyerKH Pandya10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians
19346712371811133SS IyerRR PantKH Pandya10100NaNNaNNaNNaNDelhi CapitalsMumbai Indians

Duplicate rows

Most frequently occurring

idinningoverballbatsmannon_strikerbowlerbatsman_runsextra_runstotal_runsnon_boundaryis_wicketdismissal_kindplayer_dismissedfielderextras_typebatting_teambowling_team# duplicates
0419152131SR TendulkarC MadanPJ Sangwan10100NaNNaNNaNNaNMumbai IndiansDelhi Daredevils2